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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: DJEDDI, Warith Eddine; HERMI, Khalil; BEN YAHIA, Sadok; DIALLO, Gayo;

    We proposed an innovative approach for predicting DTIs, leveraging both contextual and local strategies. In terms of contextual information, DTIOG utilizes KGE techniques such as the DistMult model to generate drug and target embedding vectors. These vectors are derived from the knowledge graph, capturing associations and similarities between drugs and targets. Instead, DTIOG uses ProtBERT, a language model that has already been trained on protein sequences, to figure out how amino acids in proteins are put together. These embeddings, obtained through either KGE or ProtBERT, are integrated into the process of prediction. Adding to that, some recent deep learning-based models that have been pre-trained on a large corpus of protein sequences, such as ProtBERT, have been utilized to extract features of the proteins. For example, ProtBERT can be used to provide meaningful, context-aware representations of protein sequences, which are crucial for the accurate identification of lysine glutarylation sites. For the local strategy, DTIOG gets information about drugs by using the RDKit library to turn SMILES representations into molecular fingerprints. The Avalon fingerprint generator identifies specific fragments within the molecular structure, creating numerical representations for each drug. Regarding protein sequences, DTIOG processes them into feature vectors based on amino acid biochemical properties. A sliding window of size 3 categorizes amino acids into groups (i.e., non-polar, polar neutral, acidic, and basic), transforming sequences into numerical representations.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2023
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2023
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: DJEDDI, Warith Eddine; HERMI, Khalil; BEN YAHIA, Sadok; DIALLO, Gayo;

    We proposed an innovative approach for predicting DTIs, leveraging both contextual and local strategies. In terms of contextual information, DTIOG utilizes KGE techniques such as the DistMult model \cite{yang2014embedding} to generate drug and target embedding vectors. These vectors are derived from the knowledge graph, capturing associations and similarities between drugs and targets. Instead, DTIOG uses ProtBERT, a language model that has already been trained on protein sequences, to figure out how amino acids in proteins are put together. These embeddings, obtained through either KGE or ProtBERT, are integrated into the process of prediction. Adding to that, some recent deep learning-based models that have been pre-trained on a large corpus of protein sequences, such as ProtBERT, have been utilized to extract features of the proteins. For example, ProtBERT can be used to provide meaningful, context-aware representations of protein sequences, which are crucial for the accurate identification of lysine glutarylation sites. For the local strategy, DTIOG gets information about drugs by using the RDKit library to turn SMILES representations into molecular fingerprints. The Avalon fingerprint generator identifies specific fragments within the molecular structure, creating numerical representations for each drug. Regarding protein sequences, DTIOG processes them into feature vectors based on amino acid biochemical properties. A sliding window of size 3 categorizes amino acids into groups (i.e., non-polar, polar neutral, acidic, and basic), transforming sequences into numerical representations.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Software . 2023
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Software . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Orchard, Francisco; Sentis, Alexis; Sevin, Etienne;

    Project Source Code at https://github.com/pandem2 Package published at https://pypi.org/project/pandem-source Description Identify, map and integrate multiple pandemic related data into a coherent pandemic-management database. Developed within the H2020 project PANDEM-2, Pandem-Source allows users to systematically capture, standardize and analyze data coming from international and national surveillance databases, participatory surveillance projects, social networks and mass media. This tool is focused on flexibility so adding new sources or variables can be easily done as is required during a pandemic episode. Target users Public health data experts and data managers needing integrate several surveillance data. Approach Data integration follow a semantic approach. Data sources need to be described using a Data Labelling Schema (DLS) file that specifies the acquisition chennel (URL, git, local file or script) the format and how to map the input data to Pandem-Source variables. If further customistions are needed, the user can define them using simple python scripts. The DLS ensures each source and variable is properly documented knwing its origin, meaning and data quality.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2023
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2023
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Alfonsi, Tommaso; Bernasconi, Anna; Chiara, Matteo; Ceri, Stefano;

    Data and code for preprint paper Data-driven recombination detection in viral genomes. Alfonsi T., Bernasconi A., Chiara M., Ceri S.. bioRxiv 2023.06.05.543733; doi: https://doi.org/10.1101/2023.06.05.543733. The repository contains: - A manuscript_supplementary_material, containing also a guide to the files - The RecombinHunt application, with: a README file including system requirements, installation guide (running recombinhunt-3.3.3-py3-none-any.whl), example code snippets; a demo folder with Jupyter notebook (Python code) and example input/output datasets; a src folder containing the source code; a environments folder with preprocessed datasets and lineage/mutation probability tables and a validation_data folder with supporting files.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2023
    License: CC BY NC ND
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2023
    License: CC BY NC ND
    Data sources: ZENODO
    ZENODO
    Software . 2023
    License: CC BY NC ND
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2023
      License: CC BY NC ND
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2023
      License: CC BY NC ND
      Data sources: ZENODO
      ZENODO
      Software . 2023
      License: CC BY NC ND
      Data sources: Datacite
      addClaim

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Karki, Reagon;

    A Knowledge graph representation of compounds and associated biological entities in the BY-COVID and EOSC Future project. Current status Number of Nodes: 35952 Number of Edges: 279462 Human Proteins: 1347 Assay: 15835 Chemical/Compound: 4096 Mechanism of Action: 739 Pathway: 1513 Disease: 1585 SideEffect: 7420 Biological Process: 2085 Molecular Function: 1332 Please check the BY_COVID_update_August.ipynb for understanding step wise process of KG generation and KG statistics. The KG has been exported to formats such as graphml, sif and so on for visualizations in other platforms. For example, the graphml file can be imported to Cytoscape directly. These files are located in 'data\export' folder.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2022
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2022
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2022
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Software . 2022
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Muhammad Asad; Reuben Dorent; Tom Vercauteren;

    FastGeodis provides efficient CPU (OpenMP) and GPU (CUDA) implementations of Generalised Geodesic Distance Transform in PyTorch for 2D and 3D input data based on parallelisable raster scan ideas from [1]. It includes methods for computing Geodesic, Euclidean distance transform and mixture of both. [1] Criminisi, Antonio, Toby Sharp, and Khan Siddiqui. “Interactive Geodesic Segmentation of n-Dimensional Medical Images on the Graphics Processor.” Radiological Society of North America (RSNA), 2009.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Software . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Poledna, Sebastian; Miess, Michael Gregor; Hommes, Cars; Rabitsch, Katrin;

    data_and_programs.zip contains the data sets and programs used to run the models in the article "Economic forecasting with an agent-based model". additional_data_and_programs_dsge.zip and additional_data_and_programs_dsge_shadow_rate.zip contain the Dynare .mod files of the DSGE models and output variables. additional_data_eurostat.zip contains the Eurostat data tables downloaded from the bulk download facility used in the calibration, estimation, and validation of the models and additional_data_and_programs_vsc.zip the C source code (generated with the MATLAB Coder), compiled program, and simulation results from the Vienna Scientific Cluster (VSC).

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2022
    License: CC BY NC ND
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Software . 2022
    License: CC BY NC ND
    Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Galpin, Vashti;

    Scottish COVID-19 Data Curation Interface This Links prototype provides an interface to curate the weekly data available from National Records of Scotland. It allows for the uploading of individual CSV files, adds new data to the database, and indicates when there are new values for existing data. It uses the temporal features of Links to provide valid-time information about fatality counts. It also displays the data, as well as changes to the data, at three levels. Individual counts (specified by the week and subcategory that the data applies to) Grouped by week Grouped by subcategory It supports the answering of questions such as the following. How do the Male and Female subcategories compare in terms of the change in fatalities? How do the Male and Female subcategories compare in terms of number of updates to existing values? Note that this is a prototype hence some functionality has not been implemented and not all buttons in the interface will respond. The CSV file uploading makes the assumption that uploads will occur in the order the files were originally released, and unexpected behaviour may occur for a different ordering of uploads. Additionally, the CSV files for upload which can be found in the data directory have been modified, and uploading the CSV files from the original site will result in errors.

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    Authors: Faraudo, Jordi; Sahihi, Mehdi;

    PDB files of initial and final structures for the simulations reported in Biorxiv preprint "Computer Simulation of the interaction between SARS-CoV-2 Spike Protein and the Surface of Coinage Metals" Mehdi Sahihi and Jordi Faraudo doi: https://doi.org/10.1101/2022.07.28.501856

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bernal-Delgado, Enrique; Estupiñán-Romero, Francisco; González-Galindo, Javier; Tellería-Orriols, Carlos; +1 Authors

    The PHIRI Federated Research Infrastructure (FRI) is supported by a containerized reproducible solution for data analysis to be deployed on-premises by each participant partner (a.k.a PHIRI-app). This solution is based on identifying the relevant data sources for each case study (including this demonstration pilot), developing the common data models and analytical pipelines, and enabling the FAIR reporting of the rapid cycle outputs. The R Markdown script is provided, integrated within the PHIRI app, for PHIRI Pilot regional analyses producing a dashboard for monitoring the evolution of the pandemic by rapid-cycle analysis implementation on the last available data. Here, the R Markdown script is provided with the following: a) synthetic dataset build following the specifications from the demonstration Pilot common data model, b) instructions on where to find the synthetic dataset within the demonstration Pilot Data Model description (HTML), and c) an HTML interactive report produced by performing the analyses proposed within the R Markdown using the synthetic dataset above. These elements facilitate collaboration on testing and improving the analytical pipeline within PHIRI WP7. Please get in touch with the WP7 Coordinator through the PHIRI website to contribute to developing the PHIRI- Pilot. The script (software) is offered "as-is", without warranty, and disclaims liability for damages resulting from using it. Software is released under the CC-BY-4.0 licence, which gives you permission to use the content for almost any purpose (but does not grant you any trademark permissions), so long as you note the license and give credit. {"references": ["https://doi.org/10.5281/zenodo.5148467", "https://doi.org/10.5281/zenodo.6936063"]}

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: DJEDDI, Warith Eddine; HERMI, Khalil; BEN YAHIA, Sadok; DIALLO, Gayo;

    We proposed an innovative approach for predicting DTIs, leveraging both contextual and local strategies. In terms of contextual information, DTIOG utilizes KGE techniques such as the DistMult model to generate drug and target embedding vectors. These vectors are derived from the knowledge graph, capturing associations and similarities between drugs and targets. Instead, DTIOG uses ProtBERT, a language model that has already been trained on protein sequences, to figure out how amino acids in proteins are put together. These embeddings, obtained through either KGE or ProtBERT, are integrated into the process of prediction. Adding to that, some recent deep learning-based models that have been pre-trained on a large corpus of protein sequences, such as ProtBERT, have been utilized to extract features of the proteins. For example, ProtBERT can be used to provide meaningful, context-aware representations of protein sequences, which are crucial for the accurate identification of lysine glutarylation sites. For the local strategy, DTIOG gets information about drugs by using the RDKit library to turn SMILES representations into molecular fingerprints. The Avalon fingerprint generator identifies specific fragments within the molecular structure, creating numerical representations for each drug. Regarding protein sequences, DTIOG processes them into feature vectors based on amino acid biochemical properties. A sliding window of size 3 categorizes amino acids into groups (i.e., non-polar, polar neutral, acidic, and basic), transforming sequences into numerical representations.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: DJEDDI, Warith Eddine; HERMI, Khalil; BEN YAHIA, Sadok; DIALLO, Gayo;

    We proposed an innovative approach for predicting DTIs, leveraging both contextual and local strategies. In terms of contextual information, DTIOG utilizes KGE techniques such as the DistMult model \cite{yang2014embedding} to generate drug and target embedding vectors. These vectors are derived from the knowledge graph, capturing associations and similarities between drugs and targets. Instead, DTIOG uses ProtBERT, a language model that has already been trained on protein sequences, to figure out how amino acids in proteins are put together. These embeddings, obtained through either KGE or ProtBERT, are integrated into the process of prediction. Adding to that, some recent deep learning-based models that have been pre-trained on a large corpus of protein sequences, such as ProtBERT, have been utilized to extract features of the proteins. For example, ProtBERT can be used to provide meaningful, context-aware representations of protein sequences, which are crucial for the accurate identification of lysine glutarylation sites. For the local strategy, DTIOG gets information about drugs by using the RDKit library to turn SMILES representations into molecular fingerprints. The Avalon fingerprint generator identifies specific fragments within the molecular structure, creating numerical representations for each drug. Regarding protein sequences, DTIOG processes them into feature vectors based on amino acid biochemical properties. A sliding window of size 3 categorizes amino acids into groups (i.e., non-polar, polar neutral, acidic, and basic), transforming sequences into numerical representations.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Orchard, Francisco; Sentis, Alexis; Sevin, Etienne;

    Project Source Code at https://github.com/pandem2 Package published at https://pypi.org/project/pandem-source Description Identify, map and integrate multiple pandemic related data into a coherent pandemic-management database. Developed within the H2020 project PANDEM-2, Pandem-Source allows users to systematically capture, standardize and analyze data coming from international and national surveillance databases, participatory surveillance projects, social networks and mass media. This tool is focused on flexibility so adding new sources or variables can be easily done as is required during a pandemic episode. Target users Public health data experts and data managers needing integrate several surveillance data. Approach Data integration follow a semantic approach. Data sources need to be described using a Data Labelling Schema (DLS) file that specifies the acquisition chennel (URL, git, local file or script) the format and how to map the input data to Pandem-Source variables. If further customistions are needed, the user can define them using simple python scripts. The DLS ensures each source and variable is properly documented knwing its origin, meaning and data quality.

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    Authors: Alfonsi, Tommaso; Bernasconi, Anna; Chiara, Matteo; Ceri, Stefano;

    Data and code for preprint paper Data-driven recombination detection in viral genomes. Alfonsi T., Bernasconi A., Chiara M., Ceri S.. bioRxiv 2023.06.05.543733; doi: https://doi.org/10.1101/2023.06.05.543733. The repository contains: - A manuscript_supplementary_material, containing also a guide to the files - The RecombinHunt application, with: a README file including system requirements, installation guide (running recombinhunt-3.3.3-py3-none-any.whl), example code snippets; a demo folder with Jupyter notebook (Python code) and example input/output datasets; a src folder containing the source code; a environments folder with preprocessed datasets and lineage/mutation probability tables and a validation_data folder with supporting files.

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